title: Classifying the speech response of the brain using Gaussian hidden markov model (HMM) with independent component analysis (ICA).
abstract: The purpose of this paper is to determine whether electroencephalograpy (EEG) can be used as a tool for hearing impairment tests such as hearing screening. For this study, we recorded EEG responses to two syllables, /a/ and /u/, in Korean from three subjects at Gwangju Institute of Science and Technology. The ultimate goal of this study is to classify speech sound data regardless of their size using EEG; however, as an initial stage of the study, we classified only two different speech syllables using Gaussian hidden markov model. The result of this study shows a possibility that EEG could be used for hearing screening and other diagnostic tools related to speech perception.
authors.forename: Jongin
authors.initials: J
authors.lastname: Kim
authors.forename: Suh-Kyung
authors.initials: SK
authors.lastname: Lee
authors.forename: Boreom
authors.initials: B
authors.lastname: Lee
mesh_terms: D000161:Acoustic Stimulation; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D006801:Humans; D008297:Male; D008390:Markov Chains; D016011:Normal Distribution; D010700:Phonetics; D013060:Speech / Q000502:physiology*; D013067:Speech Perception; D013997:Time Factors